Data validation

What is Data validation? (Definition)

Data validation is a process that checks information to ensure it is accurate, complete, and correctly formatted. It acts as a filter when you enter data, import files, or prepare to publish products to a webshop. You can set specific rules to catch mistakes before they cause problems. Common checks include: * Ensuring a price is a number rather than text. * Checking that a SKU follows your company's naming pattern. * Confirming a product weight stays within a realistic range. * Verifying that descriptions exist for every required language. This process keeps your database clean and prevents customers from seeing incorrect information. Systems like WISEPIM automate these checks to help maintain high data quality across all your sales channels.

Why Data validation is Important for E-commerce

Data validation is a quality control process that checks product information for errors or missing details. It prevents wrong information from reaching the webshop, which reduces customer returns and complaints. If a product description lists the wrong size or material, the customer will likely send the item back. This costs the business money and damages its reputation. A PIM system uses validation rules to act as a filter for all incoming data. WISEPIM helps automate these checks to ensure every price and specification is correct before it goes live.

Examples of Data validation

• 1A rule that ensures all product prices are positive numbers and use the correct currency format.
• 2A check that confirms every product image link leads to an actual image file.
• 3A requirement to choose a brand name from a list of approved options.
• 4A check to make sure a new product's launch date is not set in the past.
• 5A rule that ensures every product has its own unique SKU so no two items share the same code.

Tips for Data validation

• Create clear data rules for your company before you set up validation. This ensures everyone follows the same standards.
• Check your data at every step. Validate it during entry, during imports, and right before you publish it to your sales channels.
• Focus on the most important information first. Prioritize checks for prices, SKUs, and product IDs since these directly affect your sales and shipping.
• Write helpful error messages that explain how to fix the mistake. Avoid using vague error codes that confuse the user.
• Review your validation rules regularly. Update them as your business changes or when you find new types of common data errors.

Trends Surrounding Data validation

• AI-powered validation: Leveraging AI and machine learning to automatically detect anomalies, suggest validation rules, and predict potential data errors based on historical patterns.
• Automated data cleansing and enrichment: Integration of validation with automated processes that not only flag errors but also suggest or apply corrections and enrich missing data.
• Real-time and continuous validation: Shifting from periodic or batch validation to immediate, continuous checks at every point of data interaction, ensuring data quality from creation to publication.
• Headless commerce implications: Increased need for robust API-driven validation to ensure consistent data quality across diverse frontends and channels in a headless architecture.
• Sustainability data validation: Development of specific validation rules and frameworks for product sustainability attributes (e.g., certifications, material origins, carbon footprint data) to meet evolving regulatory and consumer demands.

Tools for Data validation

• WISEPIM: A PIM system that offers extensive data validation capabilities, allowing businesses to define custom rules to ensure product data quality and consistency before omnichannel distribution.
• Akeneo PIM: Provides a robust framework for defining and enforcing product data validation rules, supporting data enrichment and quality management workflows.
• Salsify: A Product Experience Management (PXM) platform with built-in data validation features to ensure product content accuracy and completeness across various sales channels.
• Talend: A data integration and data quality tool that includes powerful capabilities for data profiling, cleansing, and validation across diverse data sources.
• Magento / Shopify: E-commerce platforms that offer basic product data validation out-of-the-box, often extended by third-party plugins for more sophisticated validation logic.

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